Slides

What is a path?

The term no one really defined

• What is data science?

Data science is the discipline of making data useful. Ok…so what is it?

Three tracks of data science

1. Engineering (infrastructure and production): the process of making everything else possible

2. Analysis: the process of turning raw information into insights in a fast way

3. Modeling/Inference: the process of diving deeper into the data to discover the pattern we don’t easily see

(It is a group work from https://github.com/brohrer/academic_advisory/blob/master/authors.md !)

Engineering

• Data environment: data storage, Kafka platform, Hadoop and Spark cluster etc.

• Data management: parsing the logs, web scraping, API queries, and interrogating data streams.

• Production: integrate model and analysis into the production system

Analysis

• Domain knowledge

• Exploratory analysis

• Story telling

Modeling/Inference

• Statistical Inference

• Supervised learning

• Unsupervised learning

• Customized model development

Some confusions and more to come

• Data infrastructure engineer
• Data engineer
• BI engineer
• Data analyst: SQL, basic statistics, data visualization.
• Data scientist: R/Python, SQL, basic + applied statistics, data visualization, experimental design
• Research scientist: R/Python, advanced statistics + experimental design, ML, research background, publications, conference contributions, algorithms (Leetcode Easy/Med).
• Applied scientist: Similar to research scientist, ML algorithm design, often with an expectation of basic software engineering skills.
• Machine Learning Engineer: More advanced software engineering skillset, algorithms, machine learning algorithm design, system design. (Leetcode of all levels).
• …

Three tracks of data science

Three tracks of data science

Three tracks of data science

Data Science Hierarchy of Needs

Data Science Types v.s Needs

Where does data science belong in your organization?

• An autonomous unit parallel to engineering
• A strategic differentiator for the organization
• The head of data science unit should ideally report into the CEO or at least report into someone who understands data strategy and is willing to invest to give it what it needs.
• Pros:
  • autonomy
  • well positioned to tackle whatever problems it deems most valuable
  • knowledge share and grow
  • attract/retain talent
• Cons: risk of marginalization

Where does data science belong in your organization?

• Bring in talented people and farms them out to the rest of the company. There’s still a head of data science, but he/she is mostly a hiring manager and coach.
• Give up autonomy to ensure utility
• Pros:
  • closer to applications
  • flexibility
• Cons:
  • hard to attract/retain talent
  • their embedded team leads don’t feel responsible for their growth and happiness, while their managers won’t feel directly vested in their work
  • data scientists are second-class citizens everywhere

Where does data science belong in your organization?

• No separate data science team; each team hires and manages its own data scientists.
• Pros:
  • optimizes for organizational alignment;
• Cons:
  • dilutes the identity of data science;
  • hurt data scientist’s career growth;
  • sacrifice the flexibility of the embedded model

Startup v.s. Mature company (Pre IPO/Public)

Startup v.s. Mature company (Pre IPO/Public)

Regardless of the company stage…

• Every company claims to be data driven, but not every one is…

Regardless of the company stage…

Excerpt from How Airbnb Democratizes Data Science With Data University:

Regardless of the company stage…

• Every company claims to be data driven, but not every one is…
• How do you want to be driven by data?
• Team matters!
• Happy families are all alike; every unhappy family is unhappy in its own way
• If you want to be Google employee #20, you need to join Google when it had only 19 employees

Data Science Curriculum Roadmap

Questions